Information processing device, control method for information processing device, and program

Abstract

To provide an information processing device, a control method for the information processing device, and a program that can estimate the occurrence of visually-induced motion sickness from the components of an HMD.SOLUTION: An information processing device 101 includes an HMD configuration information acquisition unit 301 that acquires HMD configuration information indicating the relationship between the components of the HMD and visually-induced motion sickness, an induction level estimation unit 307 that estimates whether a user using the HMD is to experience visually-induced motion sickness on the basis of the induction level of visually-induced motion sickness acquired using the HMD configuration information, and an output unit 308 that outputs the estimation result of the induction level estimation unit 307.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, a control method for an information processing device, and a program. [Background technology]

[0002] In recent years, head-mounted displays (hereinafter referred to as "HMDs") have become widespread, allowing users to easily experience immersive XR (Extended Reality / Cross Reality) content. There are various types of HMDs, including standalone types that are used independently and external device-connected types that are used by connecting to PCs, smartphones, etc. Furthermore, the display viewing angle, refresh rate, display resolution, etc. that display XR content also vary depending on the type of HMD.

[0003] However, because HMDs are devices that present images across a wide field of view, users using HMDs may experience visually-induced sickness, which is similar to motion sickness and includes headaches, dizziness, and nausea. Visually-induced sickness is said to be caused by multiple factors, including HMD components that affect HMD performance, the content, and user characteristics. Patent Document 1 discloses a technology that uses a learning model to detect VR content that may induce discomfort and nausea equivalent to visually-induced sickness. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-51756 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology disclosed in Patent Document 1 has a problem in that it cannot be adapted to various types of HMDs because it does not use the components of the HMD as learning data.

[0006] The present invention has been made in view of the above-mentioned problems, and aims to provide an information processing device, a control method for the information processing device, and a program that can estimate the occurrence of visually induced motion sickness from the components of an HMD. [Means for solving the problem]

[0007] In order to achieve the above object, the information processing device of the present invention is characterized by comprising an HMD information acquisition means for acquiring HMD configuration information indicating the relationship between the components of an HMD and visually-induced motion sickness, an estimation means for estimating whether a user using an HMD will experience visually-induced motion sickness based on the degree of visually-induced motion sickness induction acquired using the HMD configuration information, and an output means for outputting the estimation result of the estimation means. [Effects of the Invention]

[0008] According to the present invention, the occurrence of visually induced motion sickness can be estimated from the components of an HMD. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing a user wearing an information processing device and a virtual space or the like displayed on the information processing device. [Figure 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of an information processing device. [Figure 3] FIG. 2 is a functional block diagram illustrating an example of a functional configuration of an information processing device. [Figure 4] FIG. 10 is a diagram illustrating an example of a feature amount history table. [Figure 5] 10 is a flowchart illustrating an example of the flow of a trained model generation process. [Figure 6] 10 is a flowchart showing an example of the flow of a process for estimating the induction of visually-induced motion sickness; DETAILED DESCRIPTION OF THE INVENTION

[0010] Each embodiment of the present invention will be described in detail below with reference to the drawings. However, the configurations described in each of the following embodiments are merely examples, and the scope of the present invention is not limited to the configurations described in each embodiment. For example, each component constituting the present invention can be replaced with any configuration that can perform the same function. Any component may also be added. Any two or more configurations (features) of each embodiment can be combined. Not all combinations of features described in each embodiment are necessarily essential to the solution of the present invention. The configurations of each embodiment can be modified or changed as appropriate depending on the specifications of the device to which the present invention is applied and various conditions (such as usage conditions and usage environment). In the following embodiments, the same components are described with the same reference numerals.

[0011] First Embodiment A first embodiment will be described below with reference to FIGS. 1 to 6. Here, as an example of estimating the occurrence of visually-induced motion sickness and notifying the user, a scene in which the user is encouraged to take a break through an HMD worn by the user will be described with reference to FIG. 1. FIG. 1 is a diagram showing a user 102 wearing an information processing device 101 and a virtual space 103 displayed on the information processing device 101. As shown in FIG. 1, the information processing device 101 is a head-mounted HMD. The HMD is a non-transparent type, but is not limited to this and may be an optically transparent type or a video transparent type. Note that the concept of "HMD" in the present invention includes smart glasses. Therefore, the information processing device 101 is not limited to a head-mounted HMD and may be, for example, eyeglass-type smart glasses.

[0012] Furthermore, the information processing device 101 is a standalone HMD, but is not limited to the standalone type and may be, for example, an external device. Examples of external devices include electronic devices such as a PC, tablet, or smartphone connected to the HMD or smart glasses. XR content that the user 102 experiences via the information processing device 101 is displayed in the virtual space 103. If the information processing device 101 estimates that the user 102 is experiencing visually-induced motion sickness, it displays a message 104 in the virtual space 103 to encourage the user 102 to take a break.

[0013] The configuration and operation of the information processing device 101 will be described below. FIG. 2 is a block diagram showing an example of the hardware configuration of the information processing device 101. As shown in FIG. 2, the information processing device 101 has, as components, a CPU 201, a ROM 202, a RAM 203, a sensing unit 204, an imaging unit 205, a display unit 206, an operation unit 207, a communication unit 208, and a storage unit 209. The components are connected to each other via a bus 210. The CPU 201 is an arithmetic processing device that performs overall control of the information processing device 101. The CPU 201 executes various programs stored in the ROM 202 and the like to perform various processes. The ROM 202 is a read-only non-volatile memory device that stores programs (image processing programs, initial data, etc.) and parameters that do not require modification.

[0014] The RAM 203 temporarily stores input information, calculation results in image processing, and the like. The RAM 203 is a memory device that provides a working area for the CPU 201. The sensing unit 204 is a device such as a sensor. The sensing unit 204 acquires position and orientation information of the user 102, who is the user of the information processing device 101, by detecting head rotation, tilt, amount of movement, and the like. The sensing unit 204 also acquires information such as eye tracking, eye movement, number of eye closures, eye closure duration, body temperature, skin moisture, brain waves, and heart rate of the user 102, who is the user of the information processing device 101, using an infrared sensor, physiological sensor, or the like. The imaging unit 205 is an imaging device such as a camera built into the HMD and performs imaging processing. Note that if the information processing device 101 is an external device type, the imaging unit 205 also includes a webcam or the like connected to a PC or the like.

[0015] The display unit 206 is a liquid crystal display or the like. The display unit 206 displays captured images, virtual objects, characters, items, and the like. The operation unit 207 is an operation unit including operation members such as a power button or a dial. If the information processing device 101 is an external device type, the operation unit of the operation unit 207 also includes a keyboard, a mouse, and the like. The communication unit 208 transmits and receives data to and from an external device via wired communication or wireless communication (wireless LAN, local 5G, etc.). If the information processing device 101 is an external device type, the communication unit 208 can receive information acquired by the sensing unit 204 of the HMD via a network. The storage unit 209 is a readable and writable nonvolatile memory device that stores various data, various programs, and the like, as well as a feature history table and a trained model, which will be described later.

[0016] Fig. 3 is a functional block diagram showing an example of the functional configuration of the information processing device 101. As shown in Fig. 3, the information processing device 101 has an HMD configuration information acquisition unit 301, a content information acquisition unit 302, a user information acquisition unit 303, an operation input unit 304, a feature amount storage unit 305, a model generation unit 306, an induction degree estimation unit 307, and an output unit 308. As described above, each function shown in Fig. 3 can be realized by the CPU 201 executing a program, but it is not necessary for the CPU 201 to realize all functions by a program. For example, the information processing device 101 may have a dedicated processing circuit that realizes one or more functions.

[0017] The HMD configuration information acquisition unit 301 (HMD information acquisition means) acquires HMD configuration information indicating the relationship between the components of the HMD and visually-induced motion sickness from the information processing device 101. Details of the HMD configuration information will be described later. The content information acquisition unit 302 (content information acquisition means) acquires content information indicating the relationship between XR content and visually-induced motion sickness from the XR content displayed on the display unit 206 of the information processing device 101. Details of the content information will be described later.

[0018] The user information acquisition unit 303 (user information acquisition means) acquires user information indicating the relationship between the user and visually-induced motion sickness from user information stored in advance in the RAM 203, the storage unit 209, etc. The user information acquisition unit 303 also acquires position and orientation information of the user 102 as user information from the sensing unit 204. Details of the user information will be described later. The user information acquisition unit 303 also acquires physiological information of the user from the sensing unit 204. The operation input unit 304 (operation acquisition means) acquires information input by the user from the operation unit 207. Details of the information input by the user will be described later. The feature saving unit 305 stores and saves the HMD configuration information, content information, user information, physiological information of the user, and information input by the user as a feature history table in the storage unit 209, etc.

[0019] FIG. 4 is a diagram showing an example of a feature history table 401. As shown in FIG. 4, in the feature history table 401, the date and time column stores the date and time when the record was saved by the feature saving unit 305. The HMD feature 402 is HMD configuration information acquired by the HMD configuration information acquisition unit 301. In the HMD feature 402, the HMD ID column stores information such as a number that can uniquely identify the type of HMD. The viewing angle column stores the value of the HMD display viewing angle. It is known that the wider the HMD display viewing angle, the more likely it is to induce vection (visually induced self-motion sensation), which is thought to be related to visually-induced motion sickness. Furthermore, due to visual characteristics, users are more likely to notice flicker in the peripheral visual field, and when the refresh rate is low and the brightness is high, the perception of flicker may lead to visually-induced motion sickness.

[0020] The refresh rate column stores the refresh rate value of the HMD. The refresh rate, and the frame rate described below, are closely related to flicker, and it is said that slower speeds are more likely to lead to visually-induced motion sickness. The resolution column stores the display resolution value of the HMD. Even if the HMD display resolution is high, if the refresh rate or tracking performance is low, the sensory mismatch will be greater, which is said to be more likely to lead to visually-induced motion sickness. The weight column stores the weight value of the HMD. It is said that a heavy HMD puts more strain on the body and is more likely to lead to visually-induced motion sickness.

[0021] The M2P delay column stores the value of the delay time, called "Motion to Photon delay (hereinafter referred to as "M2P delay"), which is the delay time until the user's movements are reflected on the HMD display. It is said that a large M2P delay value is more likely to lead to visually-induced motion sickness. The P2P delay column stores the value of the delay time, called "Photon to Photon delay (hereinafter referred to as "P2P delay"), which is the delay time from when light (real-life video) enters the HMD camera until it is displayed as a background image on the HMD display. It is said that a large P2P delay value is more likely to lead to visually-induced motion sickness. If the HMD does not have the function to display real-life video as a background image, the P2P delay column stores "N / A".

[0022] In addition, HMD configuration information indicating the relationship between HMD components and visually-induced motion sickness may be stored in the feature history table 401. For example, since it is said that poor tracking performance of the position and orientation of the HMD, controller, hand, etc. increases the sense of incongruity with reality and is likely to lead to visually-induced motion sickness, tracking performance may be stored. Furthermore, since it is said that a difference between the user's interpupillary distance and the HMD's interpupillary distance increases the convergence angle with respect to the object the user is looking at and is likely to lead to visually-induced motion sickness, the presence or absence of a function to set the HMD's interpupillary distance may be stored. Other information that may be stored include the strength of lens distortion, display persistence, and the presence or absence of a visually-induced motion sickness reduction function. In the above description, the column for HMD features 402 stores information expressed in terms of HMD functions, but the names of components (including module names) that implement these functions may also be stored.

[0023] The content feature quantity 403 is content information acquired by the content information acquisition unit 302. In the content feature quantity 403, the content ID column stores information such as a number that can uniquely identify the type of XR content. The frame rate column stores the frame rate value of the XR content. Like the refresh rate described above, the frame rate is said to be related to visually induced motion sickness. The image jitter column stores the degree of movement of the position and orientation of the user's viewpoint expected during the XR content experience. The degree is indicated as "large, medium, small," but may also be expressed as a continuous value, such as a range of 0 to 10 points. It is known that greater image jitter is more likely to lead to visually induced motion sickness.

[0024] The viewpoint type column stores a value indicating whether the XR content is from the avatar's first-person or third-person viewpoint. The first-person viewpoint is more immersive than the third-person viewpoint, and is therefore more likely to cause sensory mismatch and lead to motion sickness. The movement method column stores a value indicating the avatar's movement method in the XR content. Movement methods include, for example, teleportation via controller operation or hand interaction, continuous movement, and forced movement while sitting in the driver's seat. It is known that some movement methods are more likely to cause vection due to sensory mismatch.

[0025] Additionally, content information indicating the relationship between XR content and visually-induced motion sickness may be stored in the feature amount history table 401. For example, values ​​indicating the movement speed or acceleration of the avatar may be stored. Also, the presence or absence of visually-induced motion sickness reduction functions, such as a gaze guidance function when the avatar is moving or a tunneling function when moving, may be stored as values.

[0026] The user feature 404 is user information acquired by the user information acquisition unit 303. In the user feature 404, a column for user ID stores information such as a number that can uniquely identify a user. A column for head movement stores a value of the amount of change in the position of the HMD over a certain period of time. The value is expressed as a continuous value ranging from 0 to 10 points, but may also be expressed as a three-dimensional vector of the position, for example. It is known that the greater the amount of head movement over a certain period of time, the more likely it is to cause visually-induced motion sickness.

[0027] The head rotation amount column stores the value of the change in HMD rotation over a certain period of time. This value is expressed as a continuous value, such as a range of 0 to 10, but may also be expressed as a three-dimensional vector of rotation, for example. It is known that the greater the amount of head rotation over a certain period of time, the more likely it is to cause visually-induced motion sickness. The age column stores the value of the user's age. It is said that the susceptibility to visually-induced motion sickness is most common between the ages of 2 and 12, decreases by around the age of 21, and increases again after the age of 50. The gender column stores a value indicating the user's gender. It is said that women are more susceptible to visually-induced motion sickness than men.

[0028] Additionally, user information indicating the relationship between the user and visually-induced motion sickness may be stored in the feature history table 401. For example, values ​​indicating information that is thought to be related to visually-induced motion sickness, such as race or health condition, may be stored. Also, values ​​indicating the frequency of HMD use by the user and the duration of HMD use may be stored. It is said that when a user repeatedly experiences an HMD, their brain becomes accustomed to it and can adapt, thereby reducing visually-induced motion sickness. The frequency of HMD use and the duration of HMD use may be aggregated by type of HMD or by type of XR content.

[0029] The induction level feature 405 is physiological information of the user and information input by the user, and corresponds to the induction level of visually-induced motion sickness. The user's physiological information is acquired by the user information acquisition unit 303 via the sensing unit 204. The information input by the user is acquired by the operation input unit 304 via the operation unit 207. The SSQ score column stores SSQ score values ​​calculated using a psychological questionnaire-style measurement method called the "Simulator Sickness Questionnaire (hereinafter referred to as "SSQ")," which is used for subjective evaluation of visually-induced motion sickness. A user experiencing XR content on the information processing device 101 answers the SSQ via the operation unit 207.

[0030] The body temperature column stores the user's body temperature value, and the heart rate column stores the user's heart rate value. It is said that the body temperature and heart rate increase due to poor physical condition associated with the onset of visually-induced motion sickness. In the first embodiment, the actual measured values ​​of body temperature and heart rate by the sensing unit 204 have been described as an example, but measurements may also be taken at normal times after the HMD is put on, and the differences from normal times may be stored in the feature history table 401. The number of eye closures column stores the number of times the user closes their eyes or the duration of eye closure over a certain period of time. It is said that the user closes their eyes due to poor physical condition associated with the onset of visually-induced motion sickness.

[0031] Other input information that is considered to be related to visually-induced motion sickness and psychological or physiological information of the user may be stored in the feature history table 401. For example, values ​​indicating information that is considered to be related to visually-induced motion sickness, such as the moisture content of the skin, electroencephalograms, or the misalignment between eye movement and the HMD image, may be stored. Also, a unique index value that combines the SSQ score and other physiological index values ​​may be defined and stored.

[0032] Returning to the explanation of Figure 3, the model generation unit 306 (model generation means) uses the feature history table 401 as learning data to train a previously prepared untrained or in-training learning model, thereby generating a trained model that acquires the degree of induction of visually induced motion sickness. That is, the model generation unit 306 generates a trained model that estimates the degree of induction by machine learning using at least one of the HMD feature 402, the content feature 403, and the user feature 404 as explanatory variables and one of the induction degree feature 405 as a response variable. The relationship between the induction degree, which is the response variable, and the other explanatory variables is expressed, for example, by the following formula (hereinafter referred to as "Formula 1"). Induction level = β0 + (display viewing angle) × β1 + (refresh rate) × β2 + (display resolution) × β3 + (weight) × β4 + ...

[0033] Here, βn (n = 0, 1, 2, 3, 4, . . . ) is a coefficient determined in the trained model. Equation 1 represents a trained model generated by machine learning using a linear regression model, but machine learning using a linear regression model is merely an example, and the machine learning method is not limited to this. Specifically, for example, the trained model may be generated by machine learning using a method other than a linear regression model, such as a support vector machine (SVM). Furthermore, the model generation unit 306 may generate multiple trained models by dividing them into predetermined categories such as by HMD, by XR content, by user, or by health condition. The model generation unit 306 stores and saves the generated trained models in the storage unit 209 or the like.

[0034] The induction degree estimation unit 307 first inputs explanatory variables into the trained model generated by the model generation unit 306, and acquires the induction degree output by the trained model. The explanatory variables include HMD configuration information acquired by the HMD configuration information acquisition unit 301, content information acquired by the content information acquisition unit 302, and user information acquired by the user information acquisition unit 303. Next, the induction degree estimation unit 307 (estimation means) compares the acquired induction degree with a predetermined threshold value, and estimates whether the user is experiencing visually-induced motion sickness.

[0035] The output unit 308 (output means) generates notification information based on the estimation result of the induction level estimation unit 307 and outputs the generated notification information to the display unit 206. At this time, the output unit 308 may change the content of the generated notification information according to the estimation result of the induction level estimation unit 307. For example, the output unit 308 changes the content of the generated notification information according to the magnitude of the induction level acquired by the induction level estimation unit 307. When the induction level is a value equivalent to mild, the output unit 308 generates and outputs notification information with content such as "Are you tired? Why not take a break?" as shown in FIG. 1. When the induction level is a value equivalent to severe, the output unit 308 generates and outputs notification information with content such as "You appear to be feeling unwell. Immediately stop the content and take a break."

[0036] Additionally, the output unit 308 (reduction means) may execute a visually-induced sickness mitigation function of the information processing device 101 or the XR content, or forcibly stop the XR content itself, depending on the estimation result of the induction level estimation unit 307. For example, the output unit 308 may adjust the parameters of each HMD component in the information processing device 101 to reduce visually-induced sickness, such as by reducing the display viewing angle or lowering the display resolution and increasing the frame rate.

[0037] The model generation unit 306 (model update means) has a function of updating the trained model. The model generation unit 306 updates the trained model, for example, by performing additional learning on the generated trained model. In this case, the output unit 308 generates request information for requesting feedback of the notification result from the user, and outputs the generated request information to the display unit 206 in addition to the above-mentioned notification information. Here, the request information is information for causing the display unit 206 to output content such as, for example, "Did you experience any symptoms of visually induced motion sickness? We will provide feedback to the induction determination."

[0038] The user performs an operation input on the operation unit 207 indicating whether or not visually-induced motion sickness is actually occurring, thereby causing the operation input unit 304 to acquire feedback information (input information). The model generation unit 306 updates the trained model based on the feedback information acquired by the operation input unit 304. For example, if the content of the feedback information is "No" as an answer to the question "Did you experience any symptoms of visually-induced motion sickness?", it can be said that the accuracy of the trained model is low. Therefore, the model generation unit 306 updates the trained model by performing supervised learning on the trained model using, for example, the feedback information acquired by the operation input unit 304 as training data.

[0039] In this way, the model generation unit 306 can improve the accuracy of the induction degree acquired from the trained model by updating the trained model. Note that the user may input an appropriate induction degree when receiving a request for feedback on the notification result. In this case, the model generation unit 306 updates the trained model so that the induction degree acquired from the trained model matches the induction degree indicated by the feedback information.

[0040] FIG. 5 is a flowchart showing an example of the flow of the trained model generation process. The flow of the trained model generation process shown in the flowchart of FIG. 5 is realized by the CPU 201 expanding a program stored in the ROM 202 or the storage unit 209 into the RAM 203 and executing it. In the trained model generation process shown in the flowchart of FIG. 5, first, in step S501, the CPU 201 acquires HMD configuration information using the HMD configuration information acquisition unit 301. In step S502, the CPU 201 acquires content information using the content information acquisition unit 302. In step S503, the CPU 201 acquires user information using the user information acquisition unit 303.

[0041] In step S504, the CPU 201 acquires physiological information (biological effect information) of the user and information input by the user (biological effect information) using the user information acquisition unit 303 (measurement acquisition means) and the operation input unit 304 (measurement acquisition means). In step S505, the CPU 201 causes the feature storage unit 305 to store the HMD configuration information acquired in step S501, the content information acquired in step S502, and the user information acquired in step S503 in the feature history table 401. Furthermore, the CPU 201 causes the feature storage unit 305 to store the physiological information of the user acquired in step S504 and the information input by the user in the feature history table 401. In step S506, the CPU 201 causes the model generation unit 306 to train a previously prepared learning model that has not been trained or is currently being trained. At this time, the CPU 201 causes the model generation unit 306 to use the feature history table 401 in which the various pieces of information are saved in step S505 as learning data.

[0042] In step S507, the CPU 201 determines whether the model generation unit 306 has completed learning of the learning model. At this time, the CPU 201 causes the model generation unit 306 to make the determination in step S507, for example, by comparing a predetermined number of learning times or a learning time. If the CPU 201 determines that the model generation unit 306 has not completed learning of the learning model, the process returns to step S501. As a result, the CPU 201 repeats the processes from step S501 to step S507 until it determines in step S507 that learning of the learning model has been completed. On the other hand, if the CPU 201 determines that the model generation unit 306 has completed learning of the learning model, the process proceeds to step S508.

[0043] In step S508, the CPU 201 generates a trained model that outputs the degree of induction of visually-induced motion sickness by using the model generation unit 306 to treat the learning model that has been trained so far as a trained model. In step S509, the CPU 201 causes the model generation unit 306 to store and save the trained model in the storage unit 209 or the like. Thereafter, the generation of the trained model shown in the flowchart of FIG. 5 ends.

[0044] Fig. 6 is a flowchart showing an example of the flow of the visually induced sickness induction estimation process. The flow of the visually induced sickness induction estimation process (control method of an information processing device) shown in the flowchart of Fig. 6 is realized by the CPU 201 (computer) loading a program stored in the ROM 202 or the storage unit 209 into the RAM 203 and executing it. Note that the visually induced sickness induction estimation process shown in the flowchart of Fig. 6 is repeatedly executed, for example, every time a predetermined period has elapsed. In the visually induced sickness induction estimation process shown in the flowchart of Fig. 6, first, in step S601, the CPU 201 acquires HMD configuration information using the HMD configuration information acquisition unit 301 (HMD information acquisition step).

[0045] In step S602, the CPU 201 acquires content information using the content information acquisition unit 302. In step S603, the CPU 201 acquires user information using the user information acquisition unit 303. In step S604, the CPU 201 inputs the information acquired in steps S601 to S603 into the trained model using the induction level estimation unit 307, and acquires the induction level output by the trained model. Specifically, the CPU 201 first acquires the trained model saved in step S509 by reading it from the storage unit 209 or the like using the induction level estimation unit 307. Next, the CPU 201 inputs the information acquired in steps S601 to S603 as an explanatory variable into the acquired trained model using the induction level estimation unit 307, and acquires the induction level to be output as a target variable.

[0046] In step S605, the CPU 201 determines, using the induction level estimation unit 307, whether visually induced sickness has occurred. Specifically, the CPU 201 determines whether visually induced sickness has occurred by comparing the induction level acquired in step S604 with a predefined threshold and determining whether the induction level exceeds the threshold. Here, if the induction level exceeds the threshold, it is determined that visually induced sickness has occurred, and if the induction level does not exceed the threshold, it is determined that visually induced sickness has not occurred. In this way, in step S605, the CPU 201 estimates, using the induction level estimation unit 307, whether the user using the HMD has experienced visually induced sickness (estimation step). If the CPU 201 determines, using the induction level estimation unit 307, that visually induced sickness has not occurred, the visually induced sickness induction estimation process shown in the flowchart of FIG. 6 ends. On the other hand, if the CPU 201 determines, using the induction level estimation unit 307, that visually induced sickness has occurred, the process proceeds to step S606.

[0047] In step S606, the CPU 201 generates notification information using the output unit 308 and outputs the generated notification information to the display unit 206 (output step). As a result, the notification information is displayed on the display unit 206. In step S607, the CPU 201 generates request information using the output unit 308 and outputs the generated request information to the display unit 206. As a result, the request information is displayed on the display unit 206. In this way, the CPU 201 requests feedback from the user regarding the content of the notification information output in step S606 using the output unit 308. In step S608, the CPU 201 updates the trained model using the model generation unit 306 in accordance with the content of the user's response to the feedback request in step S607. Thereafter, the visually induced motion sickness induction estimation process shown in the flowchart of FIG. 6 ends.

[0048] As described above, the CPU 201 repeatedly executes the visually induced motion sickness induction estimation process shown in the flowchart of Fig. 6 after a predetermined period has elapsed since the process ended. In the above description, the induction level estimation unit 307 determines whether visually induced motion sickness has occurred based on the induction level acquired from the trained model generated by the model generation unit 306, but this is not limiting. For example, the induction level estimation unit 307 (estimation means) may determine whether a user using an HMD has visually induced motion sickness using the induction level acquired from Equation 1, in which the coefficients βn (n = 0, 1, 2, 3, 4, ...) described above are predetermined.

[0049] As described above, according to the first embodiment, the information processing device 101 can estimate the occurrence of visually induced motion sickness from the components of the HMD.

[0050] Second Embodiment In the first embodiment, the CPU 201 of the information processing device 101 uses the HMD feature 402, the content feature 403, and the user feature 404 as explanatory variables to create the trained model, but the explanatory variables are not limited to these. For example, the CPU 201 of the information processing device 101 may use factors such as the usage environment, whether various functions are on or off, or whether a linked service is available as explanatory variables. The CPU 201 of the information processing device 101 may also create the trained model using only the HMD feature 402. The CPU 201 of the information processing device 101 may also generate the trained model from the HMD feature 402 and the content feature 403, or may generate the trained model from the HMD feature 402 and the user feature 404.

[0051] Third Embodiment In the first and second embodiments, the information processing device 101 estimates the occurrence of visually-induced motion sickness in the user 102 wearing an HMD in real time. However, the embodiments are not limited to this. In this regard, the information processing device 101 may be an electronic device such as a PC, tablet, or smartphone, rather than an HMD. Furthermore, instead of estimating the occurrence of visually-induced motion sickness in real time, the information processing device 101 may acquire the degree of visually-induced motion sickness induction based on the conditions entered by the user each time the user makes an input. For example, when the CPU 201 of the information processing device 101 wants to list HMDs that cause less visually-induced motion sickness for a specific user, it acquires the induction degree using the HMD configuration information of multiple candidate HMDs and the user information of the specific user as explanatory variables. The CPU 201 (sorting means) of the information processing device 101 then sorts the acquired induction degree values ​​to create a list of HMDs in descending order of visually-induced motion sickness. Furthermore, the CPU 201 of the information processing device 101 causes the output unit 308 to display the sorted list of HMDs on the display unit 206.

[0052] <Other> Although preferred embodiments of the present invention have been described above, the present invention is not limited to the above embodiments and various modifications and variations are possible within the scope of the present invention. The present invention can also be realized by supplying a program that realizes one or more functions of the above embodiments to a system or device via a network or storage medium, and having one or more processors in the computer of the system or device read and execute the program. The present invention can also be realized by a circuit (e.g., an ASIC) that realizes one or more functions.

[0053] The disclosure of each embodiment includes the following configurations, methods, and programs. (Configuration 1) An HMD information acquisition means for acquiring HMD configuration information indicating the relationship between the components of an HMD and visually induced motion sickness; an estimation means for estimating whether a user of an HMD will experience visually induced motion sickness based on the degree of visually induced motion sickness acquired using the HMD configuration information; and output means for outputting an estimation result from said estimation means. (Configuration 2) A measurement and acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information and the biological effect information as training data, 2. The information processing device according to claim 1, wherein the estimation means acquires a degree of induction of visually induced motion sickness by inputting the HMD configuration information into the trained model. (Configuration 3) An operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; and a model update means for updating the trained model using the input information. (Configuration 4) An information processing device according to configuration 2 or 3, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image. (Configuration 5) A content information acquisition unit is provided to acquire content information indicating the relationship between the XR content displayed on the HMD and visually induced motion sickness, 2. The information processing device according to configuration 1, wherein the estimation means acquires a degree of induction of visually induced motion sickness by also using the content information. (Configuration 6) A measurement and acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the content information, and the biological effect information as training data; The information processing device according to configuration 5, wherein the estimation means acquires a degree of induction of visually induced motion sickness by inputting the HMD configuration information and the content information into the trained model. (Configuration 7) An operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; and a model update means for updating the trained model using the input information. (Configuration 8) An information processing device according to configuration 6 or 7, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image. (Configuration 9) An information processing device described in any one of configurations 5 to 8, characterized in that the content information includes information indicating the relationship with visually-induced sickness for at least one of the following: frame rate, degree of image shaking, viewpoint type, avatar movement method, avatar movement speed, avatar movement acceleration, and presence or absence of a visually-induced sickness reduction function when the avatar moves. (Configuration 10) A content information acquisition means for acquiring content information indicating the relationship between the XR content displayed on the HMD and visually induced motion sickness; a user information acquisition means for acquiring user information indicating a relationship between the user and visually induced motion sickness, 2. The information processing device according to configuration 1, wherein the estimation means acquires a degree of induction of visually induced motion sickness using the content information and the user information. (Configuration 11) A measurement and acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the content information, the user information, and the biological effect information as training data; 11. The information processing device according to claim 10, wherein the estimation means acquires a degree of induction of visually induced motion sickness by inputting the HMD configuration information, the content information, and the user information into the trained model. (Configuration 12) An operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; 12. The information processing device according to configuration 11, further comprising: a model update means for updating the trained model using the input information. (Configuration 13) An information processing device according to configuration 11 or 12, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image. (Configuration 14) An information processing device described in any one of configurations 10 to 13, characterized in that the content information includes information indicating the relationship with visually-induced sickness for at least one of the following: frame rate, degree of image shaking, viewpoint type, avatar movement method, avatar movement speed, avatar movement acceleration, and presence or absence of a visually-induced sickness reduction function when the avatar moves. (Configuration 15) An information processing device according to any one of configurations 10 to 14, characterized in that the user information includes information indicating the relationship with visually-related motion sickness for at least one of the amount of head movement, amount of head rotation, age, race, health condition, frequency of HMD use, and duration of HMD use. (Configuration 16) A user information acquisition unit is provided to acquire user information indicating a relationship between a user and visually induced motion sickness, 2. The information processing device according to configuration 1, wherein the estimation means acquires a degree of induction of visually induced motion sickness by also using the user information. (Configuration 17) A measurement and acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the user information, and the biological effect information as training data; 17. The information processing device according to claim 16, wherein the estimation means acquires a degree of induction of visually induced motion sickness by inputting the HMD configuration information and the user information into the trained model. (Configuration 18) An operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; 18. The information processing device according to configuration 17, further comprising: a model update means for updating the trained model using the input information. (Configuration 19) An information processing device according to configuration 17 or 18, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image. (Configuration 20) An information processing device described in any one of configurations 16 to 19, characterized in that the user information includes information indicating the relationship with visually-related motion sickness for at least one of the following: amount of head movement, amount of head rotation, age, race, health condition, frequency of HMD use, and duration of HMD use. (Configuration 21) An information processing device described in any one of configurations 1 to 20, characterized in that the HMD configuration information includes information indicating the relationship with visually-related motion sickness for at least one of the following: display viewing angle, refresh rate, display resolution, weight, Motion to Photon delay, Photon to Photon delay, tracking performance, presence or absence of a function to set interpupillary distance, lens distortion, display persistence, and names of components that implement the functions of the HMD. (Configuration 22) The information processing device according to any one of configurations 1 to 21, wherein the estimation means estimates whether visually induced motion sickness will occur by comparing the acquired degree of visually induced motion sickness with a threshold value. (Configuration 23) The information processing device according to any one of configurations 1 to 22, wherein the output means outputs notification information generated based on the estimation result of the estimation means. (Configuration 24) The information processing device according to configuration 23, wherein the output means changes the content of the notification information depending on the estimation result of the estimation means. (Configuration 25) The information processing device according to any one of configurations 1 to 24, further comprising a mitigation unit that executes a visually-induced motion sickness mitigation function of the information processing device in accordance with the estimation result of the estimation unit. (Configuration 26) A sorting unit is provided to create a list of HMDs by sorting the HMDs by the degree of visually induced motion sickness acquired by the estimation unit. 26. The information processing apparatus according to any one of configurations 1 to 25, wherein the output means outputs the list. (Configuration 27) The information processing device according to any one of configurations 1 to 26, wherein the information processing device is an HMD. (Configuration 28) The information processing device according to any one of configurations 1 to 26, wherein the information processing device is an electronic device connected to an HMD. (Method 1) An HMD information acquisition step of acquiring HMD configuration information indicating the relationship between the components of an HMD and visually induced motion sickness; an estimation step of estimating whether a user of an HMD will experience visually-induced motion sickness based on the visually-induced motion sickness induction degree acquired using the HMD configuration information; an output step of outputting an estimation result in the estimation step. (Program 1) A program for causing a computer to execute each means of the information processing device according to any one of configurations 1 to 28. [Explanation of symbols]

[0054] 101 Information processing equipment 201 CPU (computer) (sorting means) 301 HMD configuration information acquisition unit (HMD information acquisition means) 302 Content information acquisition unit (content information acquisition means) 303 User information acquisition unit (measurement acquisition means) (user information acquisition means) 304 Operation input unit (measurement acquisition means) (operation acquisition means) 306 Model generation unit (model generation means) (model update means) 307 Induction degree estimation unit (estimation means) 308 Output unit (output means) (reduction means)

Claims

1. an HMD information acquisition means for acquiring HMD configuration information indicating the relationship between the components of the HMD and visually induced motion sickness; an estimation means for estimating whether a user of an HMD will experience visually induced motion sickness based on the visually induced motion sickness induction degree acquired using the HMD configuration information; and output means for outputting an estimation result from said estimation means.

2. a measurement acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information and the biological effect information as training data, The information processing device according to claim 1 , wherein the estimation unit acquires the degree of induction of visually induced motion sickness by inputting the HMD configuration information into the trained model.

3. an operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; The information processing device according to claim 2 , further comprising: a model update means for updating the trained model using the input information.

4. The information processing device according to claim 2, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image.

5. a content information acquisition means for acquiring content information indicating a relationship between XR content displayed on the HMD and visually induced motion sickness; The information processing apparatus according to claim 1 , wherein the estimation unit acquires a degree of induction of visually induced motion sickness by also using the content information.

6. a measurement acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the content information, and the biological effect information as training data, The information processing device according to claim 5 , wherein the estimation means acquires the degree of induction of visually induced motion sickness by inputting the HMD configuration information and the content information into the trained model.

7. an operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; The information processing device according to claim 6, further comprising: a model update means for updating the trained model using the input information.

8. The information processing device according to claim 6, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, brain waves, and discrepancy between eye movement and image.

9. The information processing device according to claim 5, characterized in that the content information includes information indicating the relationship with visually-induced sickness for at least one of the following: frame rate, degree of image shaking, viewpoint type, avatar movement method, avatar movement speed, avatar movement acceleration, and presence or absence of a visually-induced sickness reduction function when the avatar moves.

10. A content information acquisition means for acquiring content information indicating a relationship between XR content displayed on the HMD and visually induced motion sickness; a user information acquisition means for acquiring user information indicating a relationship between the user and visually induced motion sickness, The information processing apparatus according to claim 1 , wherein the estimation unit acquires the degree of induction of visually induced motion sickness by using the content information and the user information as well.

11. a measurement acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the content information, the user information, and the biological effect information as training data, The information processing device according to claim 10 , wherein the estimation means acquires the degree of induction of visually induced motion sickness by inputting the HMD configuration information, the content information, and the user information into the trained model.

12. an operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; The information processing device according to claim 11, further comprising: a model update means for updating the trained model using the input information.

13. The information processing device according to claim 11, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, electroencephalogram, and discrepancy between eye movement and image.

14. The information processing device according to claim 10, characterized in that the content information includes information indicating the relationship with visually-induced sickness for at least one of the following: frame rate, degree of image shaking, viewpoint type, avatar movement method, avatar movement speed, avatar movement acceleration, and presence or absence of a visually-induced sickness reduction function when the avatar moves.

15. 11. The information processing device according to claim 10, wherein the user information includes information indicating a relationship with visually-related motion sickness for at least one of the amount of head movement, amount of head rotation, age, race, health condition, frequency of HMD use, and duration of HMD use.

16. a user information acquisition means for acquiring user information indicating a relationship between a user and visually induced motion sickness; The information processing apparatus according to claim 1 , wherein the estimation unit acquires a degree of induction of visually induced motion sickness by also using the user information.

17. a measurement acquisition means for acquiring biological effect information indicating the biological effect of visually induced motion sickness on a user as a result of psychological or physiological measurement; a model generation means for generating a trained model by training the HMD configuration information, the user information, and the biological effect information as training data; The information processing device according to claim 16, wherein the estimation means acquires the degree of induction of visually induced motion sickness by inputting the HMD configuration information and the user information into the trained model.

18. an operation acquisition means for acquiring input information indicating whether or not the user is experiencing visually-induced motion sickness based on an operation input by the user; The information processing device according to claim 17, further comprising: a model update means for updating the trained model using the input information.

19. The information processing device according to claim 17, characterized in that the biological effect information includes information showing the results of measurements of at least one of SSQ score, body temperature, heart rate, number of eye closures, skin moisture content, electroencephalogram, and discrepancy between eye movement and image.

20. 17. The information processing device according to claim 16, wherein the user information includes information indicating a relationship with visually-related motion sickness for at least one of the amount of head movement, amount of head rotation, age, race, health condition, frequency of HMD use, and duration of HMD use.

21. 2. The information processing device according to claim 1, wherein the HMD configuration information includes information indicating a relationship with visually-induced motion sickness for at least one of a display viewing angle, a refresh rate, a display resolution, a weight, a Motion to Photon delay, a Photon to Photon delay, tracking performance, presence or absence of a function for setting an interpupillary distance, lens distortion, display persistence, and names of components that implement the functions of the HMD.

22. The information processing apparatus according to claim 1 , wherein the estimation means estimates whether visually induced motion sickness will occur by comparing the acquired degree of visually induced motion sickness with a threshold value.

23. 2. The information processing apparatus according to claim 1, wherein the output means outputs notification information generated based on the estimation result of the estimation means.

24. 24. The information processing apparatus according to claim 23, wherein said output means changes the content of said notification information in accordance with the estimation result of said estimation means.

25. 2. The information processing apparatus according to claim 1, further comprising a mitigation unit that executes a visually-induced motion sickness mitigation function of the information processing apparatus in accordance with the estimation result of the estimation unit.

26. a sorting means for creating a list of HMDs by sorting them according to the degree of visually induced motion sickness acquired by the estimation means, 2. The information processing apparatus according to claim 1, wherein said output means outputs said list.

27. 2. The information processing apparatus according to claim 1, wherein the information processing apparatus is an HMD.

28. 2. The information processing apparatus according to claim 1, wherein the information processing apparatus is an electronic device connected to an HMD.

29. an HMD information acquisition step of acquiring HMD configuration information indicating the relationship between the components of the HMD and visually induced motion sickness; an estimation step of estimating whether a user of an HMD will experience visually induced motion sickness based on the visually induced motion sickness induction degree acquired using the HMD configuration information; an output step of outputting an estimation result in the estimation step.

30. 2. A program for causing a computer to execute each means of the information processing apparatus according to claim 1.

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